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Introduction to Modern Statistics

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Re: Introduction to Modern Statistics

#81

Post by wespiser_2018 »

noelwelsh wrote: post #2

Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…

The difficulty of teaching statistics is that the maths you need to prove things are right and gain an intuitive understanding of the methods are far more advanced than what is presented in a basic stats course. Gosset came up with the t-test and proved to the world it made sense, yet we teach students to apply it in a black box way without a fundamental understanding of why it's right. That's not great pedagogy.

IMO, this is where Bayesian Statistics is far superior. There's a Curry-Howard isomorphism to logic which runs extremely deep, and it's possible to introduce using conjugate distributions with nice closed form analytical solutions. Anything more complex, well, that's what computers are for, and there are great ways (STAN) to run complex distributions that are far more intricate than frequentist methods.

Re: Introduction to Modern Statistics

#82

Post by wespiser_2018 »

mjburgess wrote: post #30

What's often missing from these introductions is when statistics will not work; and what it even means when it "works". The amount of data needed to tell between two normal is about 30 data points -- between two power-law distributions, >trillion. (And this basically scuppers the central limit theorem, on which a lot of cargo-cult stats is justified). Stats, imv, should be taught simulation-first: code up your hypoth…

"Simulation first" is how I did things when I worked in data science and bioinformatics. Define the simulation that represents "random", then see how far off the actual data is using either information theory or just a visual examination of the data and summary statistic checks. That's a fast and easy way to gut check any observation to see if there is an underlying effect, which you can then "prove" using a more sophisticated analysis.

Just raw hypothesis is just too easy to juke by overwhelming it with trials. Lots of research papers have "statistically significant" results, but give no mention of how many experiments it took to get them, or any indiciation of negative results. Eventually, there will always be the analysis where you incorrectly reject the null hypothsis given enough effort.

Re: Introduction to Modern Statistics

#83

Post by wespiser_2018 »

i_love_limes wrote: post #14
d00mer wrote: post #9

They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?

A lot of people... in fact a huge portion of statisticians, epidemiologists, econometrics, use it as their primary language. I do genetic epidemiology (which is considerably more compute intensive than regular epidemiology), and R is still the most common language, with the most libraries and packages being used for it, compared to python for example. I think maybe you should consider being less forthcoming with your…

I worked in data science for a few start ups, and even though I know Python (it's my LeetCode language of choice), R just dominates when it comes to accessing academic methods and computational analysis. If you are going to push the boundaries of what you can and can't analysis for statistical effects and leverage academic learnings, it's R.

Re: Introduction to Modern Statistics

#84

Post by dleeftink »

NeutralForest wrote: post #32
dleeftink wrote: post #19

Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated during each step are publication quality right out of the box. Using it truly feels like a 'fresh way' to do statistics…

there was an interview of one of the JASP (creator or maintainer, can't remember) in the "Learn Bayesian Stats" podcast; it was very interesting.

To me, it's academic software done right, both in terms of accessibility and maintenance. I'd love to hear more about their governance and funding structure and how this might be applied elsewhere, and learn about academic software of similar grade and utility.

Re: Introduction to Modern Statistics

#85

Post by fallat »

epgui wrote: post #53

As much as I appreciate and love all pedagogical endeavours in the field, especially in the form of open texts, I really, really, really dislike this overall approach to teaching introductory statistics. I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. https://li…

> I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches.

Is there anything where I can start today, as a guinea pig? My statistics education is basically zero.

Re: Introduction to Modern Statistics

#86

Post by wespiser_2018 »

epgui wrote: post #57
d00mer wrote: post #9

They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?

Probably most people who do statistics. R sucks as a language but it excels at that specific application, just because of its tremendous ecosystem (putting even python to shame in some niche areas).

R is fine, it's no more absurd than other non-typed languages like javascript. Most languages are very good at one or two things, then not so good or appropriate for other tasks. For R, that's statistics, modeling, and exploratory analysis, which it absolutely crushes at due to ecosystem effects.

Re: Introduction to Modern Statistics

#87

Post by _Wintermute »

perrygeo wrote: post #73
nomilk wrote: post #18

Earlier quoted context omitted.

Before I knew command line, I tried to install python and spent the next 3 days resolving an installation issue with 'wheel'. By contrast, from first downloading R to running my first R script took about 1 hour (the most difficult part was opening the 'script' pane in RStudio IDE, which doesn't open by default on new installations, for some reason). There's huge demand out there for statistical software that's access…

R and CRAN really get package management right. Even as a very infrequent R user, there are no surprises, it "just works". Compare that to my daily Python usage where I am continually flummoxed by dependency issues.

Strong disagree, there's a reason RStudio/Posit are spending so much time trying to develop 3rd party alternatives to install.packages() and CRAN.

Try installing an older version of a package without it pulling in the most recent incompatible dependencies, it's a whole adventure.

Re: Introduction to Modern Statistics

#88

Post by RSMDZ »

mjburgess wrote: post #30

What's often missing from these introductions is when statistics will not work; and what it even means when it "works". The amount of data needed to tell between two normal is about 30 data points -- between two power-law distributions, >trillion. (And this basically scuppers the central limit theorem, on which a lot of cargo-cult stats is justified). Stats, imv, should be taught simulation-first: code up your hypoth…

>> between two power-law distributions, >trillion

Do you have anywhere I can read more about this? I would have assumed that a trillion data points would be sufficient to compare any two real-world distributions

Re: Introduction to Modern Statistics

#89

Post by mindcrime »

dleeftink wrote: post #19

Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated during each step are publication quality right out of the box. Using it truly feels like a 'fresh way' to do statistics…

Even better than just being "free to use" it's F/OSS (under the AGPL):

https://github.com/jasp-stats/jasp-desktop

Re: Introduction to Modern Statistics

#90

Post by zozbot234 »

wespiser_2018 wrote: post #81
noelwelsh wrote: post #2

Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…

The difficulty of teaching statistics is that the maths you need to prove things are right and gain an intuitive understanding of the methods are far more advanced than what is presented in a basic stats course. Gosset came up with the t-test and proved to the world it made sense, yet we teach students to apply it in a black box way without a fundamental understanding of why it's right. That's not great pedagogy. IMO…

Maximum likelihood (which underpins many frequentist methods) basically amounts to Bayesian statistics with a uniform prior on your parameters. And the "shape" of your prior actually depends on the chosen parametrization, so in principle you can account for non-flat priors as well.
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